Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

Steiner, Matthias; Smith, James A.; Burges, Stephen J.; Alonso, Carlos; Darden, Robert W.

doi:10.1029/1999wr900142

Cited by 232 publications

(159 citation statements)

References 78 publications

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“…Radar quantitative precipitation estimation (RQPE) has finer temporal and spatial resolutions than those of traditional gauge-based station rainfall observations and can accurately reflect the nonuniformness of the precipitation over a large area [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

Wu¹,

Zou²,

Shan³

et al. 2018

Advances in Meteorology

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Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). e new Z-R relationship is then applied to derive the RQPE over the middle and lower reaches of Yangtze River for two short-time intense rainfall cases in summer (2200 UTC 1 June 2016 and 2200 UTC 18 June 2016) and one stratiform rainfall case in winter (0000 UTC 15 December 2017), and then the comparative analyses between the RQPE and the RQPEs derived by the other two methods (the fixed Z-R relationship and the dynamical Z-R relationship based on radar reflectivity classification) are accomplished. e results show that the RQPE from the new Z-R relationship is much closer to the observation than those from the other two methods because the new method simultaneously considers the echo intensity (reflecting the size and concentration of hydrometers to a certain extent) and the echo-top height (reflecting the updraft to a certain extent). Two statistics of 720 rainfall events in summer (April to June 2017) and 50 rainfall events in winter (December 2017) over the same region show that the correlation coefficient (root-mean-squared error and relative error) between RQPE derived by the new Z-R relationship and observation is significantly increased (decreased) compared to the other two Z-R relationships. Besides, the new Z-R relationship is also good at estimating rainfall with different intensities as compared to the other two methods, especially for the intense rainfall.

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Section: Introductionmentioning

confidence: 99%

A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

Wu¹,

Zou²,

Shan³

et al. 2018

Advances in Meteorology

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“…For the purpose of model evaluations, we used the R 2 and RMSE. The R 2 between the modeled and TRMM estimates and RMSE at six different scales were calculated using Equations (11) and (12).…”

Section: Two-fold Cross Validationmentioning

confidence: 99%

“…The DAYMET model uses daily weather observations (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997) to produce climate grids of annual total precipitation and other climatic variables over the continental US [11]. Still, these rain gauge-based methods are accurate only within the area where rain gauge stations are spatially, densely installed [12,13]. Furthermore, weather radars offer an enormous potential to improve the quality of rainfall at high resolution.…”

Section: Introductionmentioning

confidence: 99%

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

et al. 2015

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Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km 2 ) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions.

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“…Careful physically based data processing should be applied to the radar data prior to any attempt to correct residual biases by merging radar and rain gauge data. Also, careful quality control of rain gauge data is crucial [Steiner et al, 1999]. When these two conditions are met, a successful gauge adjustment can partly correct the residual errors caused by the choice of an incorrect Z-R relationship, attenuation, improper radar calibration, or partial beam blockages [Gjertsen et al, 2004].…”

Section: Introductionmentioning

confidence: 99%

Rainfall estimation by rain gauge‐radar combination: A concurrent multiplicative‐additive approach

García-Pintado

Barberá

Erena³

et al. 2009

Water Resources Research

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[1] A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km 2 . Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

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Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

Cited by 232 publications

References 78 publications

A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

Rainfall estimation by rain gauge‐radar combination: A concurrent multiplicative‐additive approach

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